Multi-element systems such as a power generation plant can involve the complex integration of multiple elements cooperatively performing a variety of tasks in order to attain a desired output or goal. Owing to this complexity, the monitoring of such a system so as to prevent or mitigate a failure or less-than desired level of performance can itself be a complex task.
One monitoring technique in such an environment utilizes estimation models and sensors. According to this technique, the sensors generate signals from which are derived sensor vectors based on sensed measures of physical or other inputs to the system and outputs generated by the system in response to the inputs. The sensor vectors are initially used to statistically “train” the estimation model. The model provides a mathematical or statistical relationship between the inputs to the system and the corresponding outputs generated by the system. During subsequent monitoring of the system, raw data from the sensors are input into the model and compared with estimated values obtained by applying the model. A large deviation between actual values of the sensor data and the estimated values generated by the model can indicate that a system fault has occurred.
Sensor-based monitoring can be used in a variety of settings. Power generation plants, manufacturing processes, complex medical equipment, and a host of other systems and devices involving the coordinated functioning of a large number of interrelated components or processes can often be efficiently monitored and controlled through sensor-based monitoring. Indeed, sensor-based monitoring can be advantageously employed in virtually any environment in which various system-specific parameters need to be monitored over time under varying conditions.
An electrical power generation plant provides a useful example of a system that can efficiently employ sensor-based monitoring. Electrical power generation involves the complex integration of multiple power generation components that function cooperatively to generate electrical power. These components can include gas turbines, heat recovery steam generators, steam turbines, and electrical generators that in combination convert fuel-bound energy via mechanical energy into electrical energy. Important operating variables that should be closely monitored to assess the performance of the entire power plant, or one or more of its components such as a gas turbine, include pressure and temperature in various regions of the system as well as vibrations and other important parameters that reveal the condition of the equipment of the system.
Regardless of the environment in which sensor-based monitoring is utilized, the accuracy of the model employed can be a critical factor in whether the monitoring is accurate. A model's accuracy often times depends on whether the model is appropriately updated to reflect structural or other changes in the system monitored with the model. Additionally, new models may be developed that would enhance monitoring of a system. More than one model may be applied with respect to a monitored system.
The task of updating system-monitoring models is made more complex when more than one system is monitored on the basis of multiple models. If a property of an underlying model that applies to two or more systems is updated, then modifying each of the models in accordance with the updated property typically requires loading each model on one or more computing devices that perform the various model calculations for each particular system. Thus, updating estimation model properties and modification of estimation models in response to the updating typically must be performed with respect to each system separately.
Performing these tasks separately for an estimation model as it is applied to different systems can be an arduous, time consuming task for a diagnostic engineer or technician. This is especially so given that in many situations, engineers and technicians in diagnostic centers may be charged with monitoring hundreds of systems continuously or on a frequent basis. Accordingly, there is a need for a way to more effectively and efficiently update estimation properties and modify system-monitoring models in response to the updates when such estimation models are used in the monitoring of large numbers of systems.